Siting Wind Farms More Quickly and Cheaply

New model predicts wind speeds more accurately with three months of data than others do with 12.image: Jose-Luis Olivares

When a power company wants to build a new wind farm, it generally hires a consultant to make wind speed measurements at the proposed site for eight to 12 months. Those measurements are correlated with historical data and used to assess the site’s power-generation capacity.

At the International Joint Conference on Artificial Intelligence , IJCAI, later this month, MIT researchers will present a new statistical technique that yields better wind-speed predictions than existing techniques do — even when it uses only three months’ worth of data. That could save power companies time and money, particularly in the evaluation of sites for offshore wind farms, where maintaining measurement stations is particularly costly.

“We talked with people in the wind industry, and we found that they were using a very, very simplistic mechanism to estimate the wind resource at a site,” says Kalyan Veeramachaneni, a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and first author on the new paper. In particular, Veeramachaneni says, standard practice in the industry is to model correlations in wind-speed data using a so-called Gaussian distribution — the “bell curve” familiar from basic statistics.

“The data here is non-Gaussian; we all know that,” Veeramachaneni says. “You can fit a bell curve to it, but that’s not an accurate representation of the data.”

Typically, a wind energy consultant will find correlations between wind speed measurements at a proposed site and those made, during the same period, at a nearby weather station where records stretch back for decades. On the basis of those correlations, the consultant will adjust the weather station’s historical data to provide an approximation of wind speeds at the new site.

The correlation model is what’s known in statistics as a joint distribution. That means that it represents the probability not only of a particular measurement at one site, but of that measurement’s coincidence with a particular measurement at the other. Wind-industry consultants, Veeramachaneni says, usually characterize that joint distribution as a Gaussian distribution.

The researchers first applied their technique to data collected from an anemometer on top of the MIT Museum, which was looking to install a wind turbine on its roof. Once they had evidence of their model’s accuracy, they applied it to data provided to them by a major consultant in the wind industry.

With only three months of the company’s historical data for a particular wind farm site, Veeramachaneni and his colleagues were able to predict wind speeds over the next two years three times as accurately as existing models could with eight months of data. Since then, the researchers have improved their model by evaluating alternative ways of calculating joint distributions. According to additional analysis of the data from the Museum of Science, which is reported in the new paper, their revised approach could double the accuracy of their predictions.